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where S1 through S, are the c subsets of examples resulting from partitioning S by the c-valued attribute A Note that Splitlnfomzation is actually the entropy of S with respect to the values of attribute A This is in contrast to our previous uses of entropy, in which we considered only the entropy of S with respect to the target attribute whose value is to be predicted by the learned tree The Gain Ratio measure is defined in terms of the earlier Gain measure, as well as this Splitlnfomzation, as follows GainRatio(S, A) r Gain (S, A) Split Inf ormation(S, A)

PROFESSIONAL/CAREER/RESEARCH OBJECTIVE A research position that requires background in organic synthesis and/or chemical identification through spectrographic techniques such as NMR, GC, IR, UV-Vis, and mass spectroscopy EDUCATION BS, 1996, Newcomb College of Tulane University, New Orleans, LA; major in chemistry; GPA 33; cumulative GPA 32 1993 1994, University of Tennessee, Knoxville, TN Summer 1992, University of Alabama, Tuscaloosa, AL; studied biochemistry and environmental geology COURSEWORK (*denotes courses taken at the University of Tennessee) Introductory Chemistry I*, II* Organic Chemistry I*, II*, III Advanced Organic Chemistry Physical Chemistry I, II Advanced Inorganic Chemistry Independent Research in Chemistry Senior Research in Chemistry Related courses: Instrumental Methods Calculus I*, II Physics I, II Biology I*, II* Environmental Hazards Natural Hazards Environmental Geology and Natural Resources

The next piece of configuration is Package-wide settings. Click on the PackageMaker icon at the top of the left pane. This shows you the Package settings: see Figure 15 11.

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Notice that the Splitlnfomzation term discourages the selection of attributes with many uniformly distributed values For example, consider a collection of n examples that are completely separated by attribute A (eg, Date) In this case, the Splitlnfomzation value will be log, n In contrast, a boolean attribute B that splits the same n examples exactly in half will have Splitlnfomzation of 1 If attributes A and B produce the same information gain, then clearly B will score higher according to the Gain Ratio measure One practical issue that arises in using GainRatio in place of Gain to select attributes is that the denominator can be zero or very small when ISi 1 x IS1 for one of the Si This either makes the GainRatio undefined or very large for attributes that happen to have the same value for nearly all members of S To avoid selecting attributes purely on this basis, we can adopt some heuristic such as first calculating the Gain of each attribute, then applying the GainRatio test only considering those attributes with above average Gain (Quinlan 1986) An alternative to the GainRatio, designed to directly address the above difficulty, is a distance-based measure introduced by Lopez de Mantaras (1991) This measure is based on defining a distance metric between partitions of'the data Each attribute is evaluated based on the distance between the data partition it creates and the perfect partition (ie, the partition that perfectly classifies the training data) The attribute whose partition is closest to the perfect partition is chosen Lopez de Mantaras (1991) defines this distance measure, proves that it is not biased toward attributes with large numbers of values, and reports experimental studies indicating that the predictive accuracy of the induced trees is not significantly different from that obtained with the Gain and Gain Ratio measures However, this distance measure avoids the practical difficulties associated with the GainRatio measure, and in his experiments it produces significantly smaller trees in the case of data sets whose attributes have very different numbers of values A variety of other selection measures have been proposed as well (eg, see Breiman et al 1984; Mingers 1989a; Kearns and Mansour 1996; Dietterich et al 1996) Mingers (1989a) provides an experimental analysis of the relative effectiveness of several selection measures over a variety of problems He reports significant differences in the sizes of the unpruned trees produced by the different selection measures However, in his experimental domains the choice of attribute selection measure appears to have a smaller impact on final accuracy than does the extent and method of post-pruning.

This section of PackageMaker deals with settings at the package level. In Figure 15 11 I have already added the Title and Description. For this installation you will specify Easy Install Only rather than offering a custom installation (if you had some choices to consider then you would set this to Custom or allow the user to choose between Custom and Easy Install values). In the Requirements tab you can define conditions for the installation to proceed. If your application needs a lot of disk space or memory, here is where you can define the conditions. As an example, this package has a condition set that will result in failure if the destination disk has less than 10 MB of disk space available. Be careful to get your pass, fail, true, and false settings right here, by the way: this can be less than intuitive! See Figure 15 12.

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